COV scan 3 populations

1 Find regions with high covariances in each population

• From Temporal Covariance analysis -output covariances for each time period

1.1 Plot the covariances across the genome

#Find the regions with a high temporal covariance 
pops<-c("PWS","TB","SS")
winsize<-c("50k","100k","250k")
evens<-paste0("chr",seq(2,26, by=2))
cov.list<-list()
covs_all<-list()
k=1
for (p in 2: length(pops)){
    pop<-pops[p]
    for (i in 1: length(winsize)){
        iv<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/MD7000_3pops_intervals_",winsize[i],"window.csv"), row.names = 1)
        if (p==3) {
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_2017-2006_2006-1996_md7000_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov23)
            colnames(covs)[4]<-c("cov23")
            covs$index=1:nrow(covs)
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list[[k]]<-covs
            names(cov.list)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            
            y<-min(covs$cov23, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ymax<-max(covs$cov23, na.rm=T)
            ggplot(covs, aes(x=index, y=cov23, color=color))+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            ggsave(paste0("../Output/COV/",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 2.7, dpi=300) 
        }
        else {
            cov12<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov12_1996-1991_2006-1996_md7000_",winsize[i],"window.csv"), header = F)
            cov23<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov23_2017-2006_2006-1996_md7000_",winsize[i],"window.csv"), header = F)
            cov13<-read.csv(paste0("~/Projects/Pacherring_Vincent/MD7000/",pop,"_cov13_2017-2006_1996-1991_md7000_",winsize[i],"window.csv"), header = F)
            covs<-cbind(iv, cov12, cov23,cov13)
            colnames(covs)[4:6]<-c("cov12","cov23","cov13")
            covs$index=1:nrow(covs)
    
            covs$color<-"col1"
            covs$color[covs$chrom %in% evens]<-"col2"
    
            covs[sapply(covs, is.infinite)] <- NA
            covs[sapply(covs, is.nan)] <- NA
            
            cov.list[[k]]<-covs
            names(cov.list)[k]<-paste0(pop,"_",winsize[i])    
            k=k+1
            covsm<-melt(covs[,c("index","color","cov12","cov23","cov13")], id.vars = c("index", "color"))
            ymax<-max(covsm$value, na.rm=T)
            y<-min(covsm$value, na.rm=T)
            ymin<-ifelse (y<=-0.1,-0.1, y) 
            ggplot(covsm, aes(x=index, y=value, color=color))+
                facet_wrap(~variable, nrow=3)+
                geom_point(size=1, alpha=0.5)+
                theme_classic()+
                ylim(ymin,ymax)+
                scale_color_manual(values=c("gray70","steelblue"), guide="none")+
                ylab("Covariance")+xlab('Chromosome')+
                theme(axis.text.x = element_blank())+
                ggtitle(paste0(pop," ", winsize[i]," window"))
            ggsave(paste0("../Output/COV/",pop,"_tempCovs_acrossGenome_",winsize[i], "Window.png"), width = 8, height = 8, dpi=300)    
        }
    }
    
}

1.2 Find the outlier regions for each time period

#find how outliers overlap between different windows
cov12<-data.frame()
cov23<-data.frame()
cov13<-data.frame()

for (i in 1:length(cov.list)){
 if (grepl("PWS",names(cov.list)[i])|grepl("TB",names(cov.list)[i])){
    covs<-cov.list[[i]]
    
    pop<-gsub("_.+",'', names(cov.list)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list)[i])
    
    covs<-covs[order(covs$cov12, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs12_top<-covs[1:n,c(1:4)]
    covs12_top<-covs12_top[order(covs12_top$chrom, covs12_top$start),]
    covs12_top$window<-win
    covs12_top$pop<-pop
    cov12<-rbind(cov12, covs12_top)
    
    covs<-covs[order(covs$cov13, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs13_top<-covs[1:n,c(1:3,6)]
    covs13_top<-covs13_top[order(covs13_top$chrom, covs13_top$start),]
    covs13_top$window<-win
    covs13_top$pop<-pop
    cov13<-rbind(cov13, covs13_top)
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:3,5)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
 }
 if (grepl("SS",names(cov.list)[i])){
    covs<-cov.list[[i]]
    
    pop<-gsub("_.+",'', names(cov.list)[i])
    win<-gsub(paste0(pop,"_"), '', names(cov.list)[i])
    
    covs<-covs[order(covs$cov23, decreasing=T),]
    n<-ceiling(nrow(covs)*0.01) #top1% region
    covs23_top<-covs[1:n,c(1:4)]
    covs23_top<-covs23_top[order(covs23_top$chrom, covs23_top$start),]
    covs23_top$window<-win
    covs23_top$pop<-pop
    cov23<-rbind(cov23, covs23_top)
    
    }
}


write.csv(cov12, "../Output/COV/3pops_top1percent_outlier_regions.cov12.csv",row.names = F)
write.csv(cov23, "../Output/COV/3pops_top1percent_outlier_regions.cov23.csv",row.names = F)
write.csv(cov13, "../Output/COV/3pops_top1percent_outlier_regions.cov13.csv",row.names = F)

#Create plots with different colors for outliers
#for COV12 and COV13 for TB and PWS (100K)
cv<-c("cov12","cov13","cov23")
winsize<-c("50k","100k","250k")

for (i in 1:length(cv)){
    if (i==1|i==2){
        for (w in 1: length(winsize)){
            #PWS
            df1<-cov.list[[paste0("PWS_", winsize[w])]]
            df1<-df1[order(df1[,cv[i]], decreasing=T),]
            n<-ceiling(nrow(df1)*0.01) #top1% region
            df1$top1<-"N"
            df1$top1[1:n]<-"PWS"
            
            #tb
            df2<-cov.list[[paste0("TB_", winsize[w])]]
            df2<-df2[order(df2[,cv[i]], decreasing=T),]
            df2$top1<-"N"
            df2$top1[1:n]<-"TB"
            
            co<-rbind(df1, df2)
    
            co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
            co$top1<-factor(co$top1, levels=c("PWS","TB","N"))
            colnames(co)[which(colnames(co)==cv[i])]<-"cov"
    
            ymax<-max(co$cov, na.rm=T)
            ggplot(co, aes(x=start/1000000, y=cov, color=top1))+
                geom_point(size=0.5)+
                facet_wrap(~chrom, ncol=4)+
                theme_classic()+ylim(-0.1,ymax)+
                scale_color_manual(values=c("deeppink","orange" ,"#ADD8E680"), labels=c("PWS", "TB", ""))+
                ylab("Covariance")+xlab('Postion (Mb)')+
                ggtitle(paste0(winsize[w]," window ",cv[i]))+
                scale_x_continuous(labels = comma)+
                guides(color = guide_legend(override.aes = list(color=c("deeppink","orange","white")), title=element_text("Top 1%")))
   
                ggsave(paste0("../Output/COV/COVscan/",cv[i],"_perChrom_",winsize[w], "Window_Outliers.png"), width = 10, height = 8, dpi=300)
        }
       
    }
   
    if (i==3){
        for (w in 1: length(winsize)){
        #pws
        df1<-cov.list[[paste0("PWS_", winsize[w])]]
        df1<-df1[,c("chrom","start","end","cov23")]
        df1<-df1[order(df1$cov23, decreasing=T),]
        n<-ceiling(nrow(df1)*0.01) #top1% region
        df1$top1<-"N"
        df1$top1[1:n]<-"PWS"
    
        #tb
        df2<-cov.list[[paste0("TB_", winsize[w])]]
        df2<-df2[,c("chrom","start","end","cov23")]
        df2<-df2[order(df2$cov23, decreasing=T),]
        df2$top1<-"N"
        df2$top1[1:n]<-"TB"
    
        #ss
        df3<-cov.list[[paste0("SS_", winsize[w])]]
        df3<-df3[,c("chrom","start","end","cov23")]
        df3<-df3[order(df3$cov23, decreasing=T),]
        df3$top1<-"N"
        df3$top1[1:n]<-"SS"

        co<-rbind(df1,df2,df3)

        co$chrom<-factor(co$chrom, levels=paste0("chr", 1:26))
        co$top1<-factor(co$top1, levels=c("PWS","TB","SS","N"))
        ymax<-max(co$cov23, na.rm=T)
        ggplot(co, aes(x=start/1000000, y=cov23, color=top1))+
            geom_point(size=0.5)+
            facet_wrap(~chrom, ncol=4)+
            theme_classic()+ylim(-0.1,ymax)+
            ylab("Covariance")+xlab('Postion (Mb)')+
            ggtitle(paste0(winsize[w]," window ",cv[i]))+
            scale_x_continuous(labels = comma)+
            #scale_color_discrete(breaks=c("PWS","SS","TB"))+
            scale_color_manual(values=c("deeppink","orange",gre,"#ADD8E666"), labels=c("PWS","TB","SS", ""))+
            guides(color = guide_legend(override.aes = list(color=c("deeppink","orange",gre,         "white")),title=element_text("Top 1% outliers"))) 
        ggsave(paste0("../Output/COV/COVscan/COV23_3Pops_perChrom_",winsize[w], "Window_Outliers.png"), width = 10, height = 9, dpi=300)
        }
    }
    
}

1.3 Run the snpEff pipeline to find annotation in the outlier regions

• Create a script to run SnpEff

Create VCF files with selected regions & run snpEff

#Create bed files
cv<-c("cov12","cov13","cov23")

for (i in 1:3){
    df<-read.csv(paste0("../Output/COV/3pops_top1percent_outlier_regions.",cv[i],".csv"))
    dfp<-df[df$pop=="PWS",]
    write.table(dfp[,1:3], paste0("../Output/COV/COVscan/PWS_outliers_",cv[i],".bed"),quote = F, row.names = F, col.names = F,sep = "\t")
    dft<-df[df$pop=="TB",]
    write.table(dft[,1:3], paste0("../Output/COV/COVscan/TB_outliers_",cv[i],".bed"),quote = F, row.names = F, col.names = F,sep = "\t")
    
    if (i==3){
        dfs<-df[df$pop=="SS",]
        write.table(dfs[,1:3], paste0("../Output/COV/COVscan/SS_outliers_",cv[i],".bed"),quote = F, row.names = F, col.names = F,sep = "\t")
        
    }
}


#create a bash script to run snpEff
bedfiles<-list.files("../Output/COV/COVscan/", pattern="*.bed")

sink("../COVscan_createVCFs.sh")
cat("#!/bin/bash \n\n")
for (i in 1:length(bedfiles)){
    fname<-gsub(".bed",'', bedfiles[i])
    cat(paste0("vcftools --gzvcf Data/new_vcf/PH_DP600_7000_minQ20_minMQ30_NS0.5_maf05.vcf.gz --bed Output/COV/COVscan/", bedfiles[i], " --out Output/COV/COVscan/", fname," --recode --keep-INFO-all \n"))
}
sink(NULL)  

#create a bash script to run snpEff
vfiles<-list.files("../Output/COV/COVscan/", pattern=".recode.vcf")

sink("~/programs/snpEff/runsnpEff_cov.sh")
cat("#!/bin/bash \n\n")
for (i in 1:length(vfiles)){
    fname<-gsub(".recode.vcf","",vfiles[i])
    cat(paste0("java -Xmx8g -jar snpEff.jar Ch_v2.0.2.99 ~/Projects/PacHerring/Output/COV/COVscan/",vfiles[i], " -stats ~/Projects/PacHerring/Output/COV/COVscan/",fname,".html >  ~/Projects/PacHerring/Output/COV/COVscan/Anno.",fname,".vcf \n"))
    
    #extract the annotation information
    cat(paste0("bcftools query -f '%CHROM %POS %INFO/AF %INFO/ANN\\n' ~/Projects/PacHerring/Output/COV/COVscan/Anno.",fname,".vcf > ~/Projects/PacHerring/Output/COV/COVscan/",fname,"_annotation \n\n"))

}
sink(NULL)  

1.3.1 Create summary gene files from snpEff and check overlapping genes.

## Create summary files of snpEff results (gene annotations in the regions of interest) and reformat as a ShinyGo input 

#create gene list 
gfiles<-list.files("../Output/COV/COVscan/", pattern="genes.txt")

for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan/",gfiles[i]), sep="\t")
    df<-df[,1:7]
    colnames(df)<-c("GeneName","GeneId","TranscriptId","BioType","variants_impact_HIGH","variants_impact_LOW",  "variants_impact_MODERATE")
    
    fname<-gsub(".genes.txt","",gfiles[i])
    genes<-unique(df$GeneId)
    sink(paste0("../Output/COV/COVscan/geneIDlist_",fname,".txt"))
    cat(paste0(genes,"; "))
    sink(NULL)
}

# Find the intersecting gene names across populations

gfiles2<-list.files("../Output/COV/COVscan/", pattern="geneIDlist")
glist<-list()
for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan/",gfiles2[i]), sep=";")
    df<-t(df)
    df<-gsub(" ","",df)
    df2<-df[!is.na(df)]
    vname<-gsub(".txt","",gfiles2[i],)
    vname<-gsub("geneIDlist_","", vname)
    glist[[i]]<-df2
    names(glist)[i]<-vname
}

times<-c("cov12","cov13","cov23")
common<-list()
common_genes<-data.frame(time=times)
for (i in 1:2){
    tlist<-glist[grep(times[i], names(glist))]
    if (i !=3){
        common[[i]]<-intersect(tlist[[1]], tlist[[2]])
        names(common)[[i]]<-times[i]
        common_genes$PWS[i]<-length(tlist[[grep("PWS", names(tlist))]])
        common_genes$TB[i]<-length(tlist[[grep("TB", names(tlist))]])
        common_genes$SS[i]<-NA
        common_genes$common_PWS.TB[i]<-length(intersect(tlist[[1]], tlist[[2]]))
    }
    if (i==3){
        common_genes$PWS[i]<-length(tlist[[grep("PWS", names(tlist))]])
        common_genes$TB[i]<-length(tlist[[grep("TB", names(tlist))]])
        common_genes$SS[i]<-length(tlist[[grep("SS", names(tlist))]])
        common_genes$common_PWS.TB[i]<-length(intersect(tlist[[1]], tlist[[3]]))
        common_genes$common_PWS.SS[i]<-length(intersect(tlist[[1]], tlist[[2]]))
        common_genes$common_SS.TB[i]<-length(intersect(tlist[[2]], tlist[[3]]))
        common_genes$common3[i]<-length(intersect(tlist[[1]],intersect(tlist[[2]], tlist[[3]])))
        k=i
        common[[k]]<-intersect(tlist[[1]], tlist[[2]])
        names(common)[[k]]<-paste0(times[i],"_PWS.SS")
        k=k+1
        common[[k]]<-intersect(tlist[[1]], tlist[[3]])
        names(common)[[k]]<-paste0(times[i],"_PWS.TB")
        k=k+1
        common[[k]]<-intersect(tlist[[2]], tlist[[3]])
        names(common)[[k]]<-paste0(times[i],"_SS.TB")
        k=k+1
        common[[k]]<-intersect(tlist[[1]],intersect(tlist[[2]], tlist[[3]]))
        names(common)[[k]]<-paste0(times[i],"_3pops")
        }
    }
}
    
write.csv(common_genes, "../Output/COV/COVscan/Common_genes_3pops.csv")

#What are the overlapping gene names

#aggregate all gene names
Genes<-data.frame()
for (i in 1:length(gfiles)){
    df<-read.table(paste0("../Output/COV/COVscan/",gfiles[i]), sep="\t")
    df<-df[,1:2]
    colnames(df)<-c("GeneName","GeneId")
    df<-df[!duplicated(df),]
    Genes<-rbind(Genes, df)
    Genes<-Genes[!duplicated(Genes),]
}

for (i in 1: length(common)){
    gids<-common[[i]]
    df<-data.frame(GeneId=gids)
    
    df<-merge(df, Genes, by="GeneId")
    write.csv(df, paste0("../Output/COV/COVscan/Common_genes_", names(common)[i],".csv"), row.names = F)
    
}

1.3.1.1 Overlapping gene numbers

time	PWS	TB	SS	common_PWS.TB	common_PWS.SS	common_SS.TB	common3
cov12	292	338	NA	30	NA	NA	NA
cov13	311	292	NA	23	NA	NA	NA
cov23	270	336	227	49	22	19	4

1.3.1.2 Overlapping regions

# Find the overlapping regions where the 4 genes belong to:
cov23_all<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov23.csv")
cov23_all<-cov23_all[cov23_all$window=="100k",]
pws<-cov23_all[cov23_all$pop=="PWS",]
tb<-cov23_all[cov23_all$pop=="TB",]
ss<-cov23_all[cov23_all$pop=="SS",]

#Overlap between PWS and TB
pws$id<-paste0(pws$chrom,"_",pws$start)
tb$id<-paste0(tb$chrom,"_",tb$start)
ss$id<-paste0(ss$chrom,"_",ss$start)
intersect(pws$id, tb$id) 
#[1] "chr13_29200000" "chr16_6400000"  "chr16_26800000" "chr18_2800000"  "chr18_12700000" "chr20_7500000" 
#[7] "chr5_13400000"  "chr9_25600000" 
#8 regions exactly match

intersect(pws$id, ss$id) 
# "chr10_10900000" "chr12_28500000" "chr18_2800000"  "chr25_1100000"  "chr3_20200000"  "chr4_31000000" 

intersect(tb$id, ss$id) 
#"chr13_22800000" "chr14_3300000"  "chr17_9700000"  "chr18_2800000"  "chr2_900000"   

intersect(pws$id, intersect(ss$id, tb$id))
#chr18_2800000"


#### Check chromosome region overlap +-100,000 bases

overlps<-data.frame()
overlps2<-data.frame()
for (i in 1: nrow(pws)){
    re2<-tb[tb$chrom==pws$chrom[i],]
    if (nrow(re2)>=1){
        for (j in 1: nrow(re2)){
            if (re2$start[j]<=pws$start[i]+100000 & re2$start[j]>=pws$start[i]-100000){
                overlps<-rbind(overlps, re2[j,])
                overlps2<-rbind(overlps2,pws[i,])}
    }}
}      


#Overlapping windows:
ov<-data.frame(id=overlps$id)
for (i in 1: nrow(overlps)){
    if (overlps$start[i]<overlps2$start[i]) {ov$start[i]<-overlps$start[i]; ov$end[i]<-overlps2$end[i]}
    if (overlps$start[i]>=overlps2$start[i]) {ov$start[i]<-overlps2$start[i];ov$end[i]<-overlps$end[i]}
}
write.csv(ov, "../Output/COV/COVscan/Overlap_regions_COV23_PWS.TB_plusminus100k.csv", row.names = F)


#### Check chromosome region overlap +-200,000 bases
overlps<-data.frame()
overlps2<-data.frame()
for (i in 1: nrow(pws)){
    re2<-tb[tb$chrom==pws$chrom[i],]
    if (nrow(re2)>=1){
        for (j in 1: nrow(re2)){
            if (re2$start[j]<=pws$start[i]+200000 & re2$start[j]>=pws$start[i]-200000){
                overlps<-rbind(overlps, re2[j,])
                overlps2<-rbind(overlps2,pws[i,])}
    }}
}      

#Overlapping windows:
ov<-data.frame(id=overlps$id)
for (i in 1: nrow(overlps)){
    if (overlps$start[i]<overlps2$start[i]) {ov$start[i]<-overlps$start[i]; ov$end[i]<-overlps2$end[i]}
    if (overlps$start[i]>=overlps2$start[i]) {ov$start[i]<-overlps2$start[i];ov$end[i]<-overlps$end[i]}
}
write.csv(ov, "../Output/COV/COVscan/Overlap_regions_COV23_PWS.TBplusminus200k.csv", row.names = F)

## PWS and SS
#### Check chromosome region overlap +-200,000 bases
overlps<-data.frame()
overlps2<-data.frame()
for (i in 1: nrow(pws)){
    re2<-ss[ss$chrom==pws$chrom[i],]
    if (nrow(re2)>=1){
        for (j in 1: nrow(re2)){
            if (re2$start[j]<=pws$start[i]+200000 & re2$start[j]>=pws$start[i]-200000){
                overlps<-rbind(overlps, re2[j,])
                overlps2<-rbind(overlps2,pws[i,])}
    }}
}      

#Overlapping windows:
ov<-data.frame(id=overlps$id)
for (i in 1: nrow(overlps)){
    if (overlps$start[i]<overlps2$start[i]) {ov$start[i]<-overlps$start[i]; ov$end[i]<-overlps2$end[i]}
    if (overlps$start[i]>=overlps2$start[i]) {ov$start[i]<-overlps2$start[i];ov$end[i]<-overlps$end[i]}
}
write.csv(ov, "../Output/COV/COVscan/Overlap_regions_COV23_PWS.SSplusminus200k.csv", row.names = F)

## SS and TB
#### Check chromosome region overlap +-200,000 bases
overlps<-data.frame()
overlps2<-data.frame()
for (i in 1: nrow(pws)){
    re2<-ss[ss$chrom==tb$chrom[i],]
    if (nrow(re2)>=1){
        for (j in 1: nrow(re2)){
            if (re2$start[j]<=tb$start[i]+200000 & re2$start[j]>=tb$start[i]-200000){
                overlps<-rbind(overlps, re2[j,])
                overlps2<-rbind(overlps2,tb[i,])}
    }}
}      
#Overlapping windows:
ov<-data.frame(id=overlps$id)
for (i in 1: nrow(overlps)){
    if (overlps$start[i]<overlps2$start[i]) {ov$start[i]<-overlps$start[i]; ov$end[i]<-overlps2$end[i]}
    if (overlps$start[i]>=overlps2$start[i]) {ov$start[i]<-overlps2$start[i];ov$end[i]<-overlps$end[i]}
}
write.csv(ov, "../Output/COV/COVscan/Overlap_regions_COV23_SS.TBplusminus200k.csv", row.names = F)


## PWS, SS and TB
#### Check chromosome region overlap +-200,000 bases
overlppw<-data.frame()
overlpss<-data.frame()
overlptb<-data.frame()
for (i in 1: nrow(pws)){
    re2<-ss[ss$chrom==pws$chrom[i],]
    re3<-tb[tb$chrom==pws$chrom[i],]
    
    if (nrow(re2)>=1& nrow(re3)>=1){
        for (j in 1: nrow(re2)){
            if (re2$start[j]<=pws$start[i]+200000 & re2$start[j]>=pws$start[i]-200000){
                for (k in 1: nrow(re3)){
                    if (re3$start[k]<=pws$start[i]+200000 & re3$start[k]>=pws$start[i]-200000){
                    overlpss<-rbind(overlpss, re2[j,])
                    overlptb<-rbind(overlptb, re3[k,])
                    overlppw<-rbind(overlppw,pws[i,])}
                }
            }
    }}
}      

ov<-overlpss[-3,]
ov$end[2]<-27000000
ov<-ov[,c(1:4,6)]
ov<-rbind(ov,ov,ov)
ov$pop[5:8]<-"PWS"
ov$pop[9:12]<-"TB"
ov$cov23[5:8]<-overlppw[c(1,2,4,5),"cov23"]
ov$cov23[9:12]<-overlptb[c(1,2,4,5),"cov23"]

write.csv(ov[,1:3], "../Output/COV/COVscan/Overlap_regions_COV23_3Pops_plusminus200k.csv", row.names = F)

sink("../Output/COV/COVscan/overlap_cov23_3pop.bed")
cat("track type=bedGraph \n")
options(scipen=999)
for (i in 1:4){
    cat(paste0(ov$chrom[i],"\t",ov$start[i], "\t", ov$end[i], "\n"))
}
sink(NULL)

p23_ano<-read.table("../Output/COV/COVscan/PWS_outliers_cov23_annotation", sep=" ")
p23_ano2<-read.table("../Output/COV/COVscan/SS_outliers_cov23_annotation", sep=" ")
p23_ano3<-read.table("../Output/COV/COVscan/TB_outliers_cov23_annotation", sep=" ")

common<-p23_ano[p23_ano$V1 %in% ov$chrom,]
common2<-p23_ano2[p23_ano2$V1 %in% ov$chrom,]
common3<-p23_ano3[p23_ano3$V1 %in% ov$chrom,]

common<-rbind(common, common2, common3)
common<-common[!duplicated(common),]

genes<-data.frame()
for (i in 1:4){
    df<-common[common$V2>=ov$start[i] & common$V2<=ov$end[i] & common$V1==ov$chrom[i],]
    genes<-rbind(genes,df)
}

# Perse the gene info
annotations<-data.frame()
for (i in 1: nrow(genes)){
    anns<-unlist(strsplit(genes$V4[i], "\\,|\\|"))
    annm<-data.frame(matrix(anns,ncol = 16, byrow = TRUE))
    annm<-annm[,c(2,3,4,5,8)]
    colnames(annm)<-c("Effect","Putative_impact","Gene_name","Gene_ID","Feature type")
    annm<-annm[!duplicated(annm), ]
    annm$chr<-genes$V1[i]
    annm$pos<-genes$V2[i]
    annm$AF<-genes$V3[i]
    annotations<-rbind(annotations, annm)
}     
annotations <-annotations[!duplicated(annotations), ]
annotations<-annotations[,c(6:8,1:5)]

write.csv(annotations, "../Output/COV/COVscan/Genes_PWSonly_outliers_100k_cov12.csv", row.names = F)
annotations<-cbind(df[,1:2], annotations)
colnames(annotations)<-c("chr", "pos", "Annotation","Putative_impact","Gene_name", "Gene_ID", "Transcript_biotype","Annotation2","Putative_impact2","Gene_name2", "Gene_ID2", "Transcript_biotype2")


ano<-annotations[!(duplicated(annotations[,3:6])),]
focus<-ano[ano$pos>=26425000& ano$pos<=26490000,]
write.csv(focus, "../Output/PCA/genes/Chr20_genes_in_26.4-26.6M.csv")

## Check chromosome overlap

2 Compare results from PWSonly and PH-3pops VCF files

pws1<-read.csv("../Output/COV/PWSonly_top1percent_outlier_cov12_overlap.csv")
pws1<-pws1[pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov12.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"PWSonly"
pws2$vcf<-"3Pops"

pws<-rbind(pws1[,c("chrom","start","end","cov12","vcf")],pws2[,c("chrom","start","end","cov12","vcf")])
ggplot(data=pws,aes(x=start, y=cov12, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point()+
    ggtitle("PWS COV12")
ggsave("../Output/COV/COVscan/PWSonly_3Pops_Outlier_overlap_cov12.png", width = 10, height = 8, dpi=300)
    
pws1<-read.csv("../Output/COV/PWSonly_top1percent_outlier_cov23_overlap.csv")
pws1<-pws1[pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov23.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"PWSonly"
pws2$vcf<-"3Pops"

pws<-rbind(pws1[,c("chrom","start","end","cov23","vcf")],pws2[,c("chrom","start","end","cov23","vcf")])
ggplot(data=pws,aes(x=start, y=cov23, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point()+
    ggtitle("PWS COV23")
ggsave("../Output/COV/COVscan/PWSonly_3Pops_Outlier_overlap_cov23.png", width = 10, height = 8, dpi=300)
    
pws1<-read.csv("../Output/COV/PWSonly_top1percent_outlier_cov13_overlap.csv")
pws1<-pws1[pws1$window=="100k",]
pws2<-read.csv("../Output/COV/3pops_top1percent_outlier_regions.cov13.csv")
pws2<-pws2[pws2$pop=="PWS"&pws2$window=="100k",]

pws1$vcf<-"PWSonly"
pws2$vcf<-"3Pops"

pws<-rbind(pws1[,c("chrom","start","end","cov13","vcf")],pws2[,c("chrom","start","end","cov13","vcf")])
ggplot(data=pws,aes(x=start, y=cov13, color=vcf, fill=vcf))+
    facet_wrap(~chrom)+
    geom_point()+
    ggtitle("PWS COV13")
ggsave("../Output/COV/COVscan/PWSonly_3Pops_Outlier_overlap_cov13.png", width = 10, height = 8, dpi=300)

3 Find